ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)

1 Setup

Instalar version en desarrollo.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")
library(emmeans)
library(corrplot)
library(multcomp)
source('https://inkaverse.com/setup.r')

session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2024-07-25
 pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
 agricolae       1.3-7    2023-10-22 [1] CRAN (R 4.4.0)
 AlgDesign       1.2.1    2022-05-25 [1] CRAN (R 4.4.0)
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 hms             1.1.3    2023-03-21 [1] CRAN (R 4.4.0)
 htmltools       0.5.8.1  2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets     1.6.4    2023-12-06 [1] CRAN (R 4.4.0)
 httpuv          1.6.15   2024-03-26 [1] CRAN (R 4.4.0)
 httr            1.4.7    2023-08-15 [1] CRAN (R 4.4.0)
 huito         * 0.2.4    2023-10-25 [1] CRAN (R 4.4.0)
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 lattice         0.22-6   2024-03-20 [2] CRAN (R 4.4.1)
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 lme4            1.1-35.5 2024-07-03 [1] CRAN (R 4.4.1)
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 magick        * 2.8.4    2024-07-14 [1] CRAN (R 4.4.1)
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 MASS          * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
 Matrix          1.7-0    2024-04-26 [2] CRAN (R 4.4.1)
 memoise         2.0.1    2021-11-26 [1] CRAN (R 4.4.0)
 mime            0.12     2021-09-28 [1] CRAN (R 4.4.0)
 miniUI          0.1.1.1  2018-05-18 [1] CRAN (R 4.4.0)
 minqa           1.2.7    2024-05-20 [1] CRAN (R 4.4.0)
 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
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 multcompView    0.1-10   2024-03-08 [1] CRAN (R 4.4.0)
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 mvtnorm       * 1.2-5    2024-05-21 [1] CRAN (R 4.4.0)
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 openssl         2.2.0    2024-05-16 [1] CRAN (R 4.4.0)
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 rappdirs        0.3.3    2021-01-31 [1] CRAN (R 4.4.0)
 Rcpp            1.0.13   2024-07-17 [1] CRAN (R 4.4.1)
 readr         * 2.1.5    2024-01-10 [1] CRAN (R 4.4.0)
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 xtable          1.8-4    2019-04-21 [1] CRAN (R 4.4.0)
 yaml            2.3.9    2024-07-05 [1] CRAN (R 4.4.1)
 zoo             1.8-12   2023-04-13 [1] CRAN (R 4.4.0)

 [1] C:/Users/LENOVO/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Refrencias

  • (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
  • (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
  • (HCPC) https://youtu.be/EJqYTDTJJug

3 Import data

https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741

url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"

gs <- url %>% 
  as_sheets_id()

imbibition <- gs %>% 
  range_read("imbibition") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(time = tiempo, .after = tiempo) %>% 
  mutate(across(1:tiempo, ~ as.factor(.)))

str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad   : Factor w/ 2 levels "criollo","Hibrido": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tiempo     : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ time       : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
##  $ peso       : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...

germination <- gs %>% 
  range_read("germination") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(across(1:variedad, ~ as.factor(.)))

str(germination)
## tibble [42 × 10] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ variedad   : Factor w/ 2 levels "criollo","Hibrido": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dia 1      : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
##  $ dia 2      : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
##  $ dia 3      : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
##  $ total      : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
##  $ pg         : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
##  $ vg         : num [1:42] 2.67 3 3 2 1.5 ...
##  $ ig         : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...

plantula <- gs %>% 
  range_read("plantula") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(across(1:variedad, ~ as.factor(.)))

str(plantula)
## tibble [210 × 16] (S3: tbl_df/tbl/data.frame)
##  $ t              : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento    : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad       : Factor w/ 2 levels "criollo","hibrido": 1 1 1 1 1 1 1 1 1 1 ...
##  $ raiz_lgtd      : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
##  $ gsr_raiz       : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
##  $ num_raiz       : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
##  $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
##  $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
##  $ alt_planta     : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
##  $ gsr_tallo      : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
##  $ nhp_hoja       : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
##  $ larg_hoja      : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
##  $ grs_hoja       : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
##  $ anch_hoja      : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
##  $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
##  $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...

4 Data summary

sm <- imbibition %>% 
  group_by(tratamiento, variedad, tiempo) %>% 
  summarise(across(peso, ~ sum(!is.na(.))))

sm
## # A tibble: 70 × 4
## # Groups:   tratamiento, variedad [14]
##    tratamiento    variedad tiempo  peso
##    <fct>          <fct>    <fct>  <int>
##  1 Agua Destilada criollo  0         30
##  2 Agua Destilada criollo  3         30
##  3 Agua Destilada criollo  6         30
##  4 Agua Destilada criollo  9         30
##  5 Agua Destilada criollo  12        30
##  6 Agua Destilada Hibrido  0         30
##  7 Agua Destilada Hibrido  3         30
##  8 Agua Destilada Hibrido  6         30
##  9 Agua Destilada Hibrido  9         30
## 10 Agua Destilada Hibrido  12        30
## # ℹ 60 more rows

sm <- germination %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(pg:ig, ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 5
## # Groups:   tratamiento [7]
##    tratamiento             variedad    pg    vg    ig
##    <fct>                   <fct>    <int> <int> <int>
##  1 Agua Destilada          criollo      3     3     3
##  2 Agua Destilada          Hibrido      3     3     3
##  3 Algas Marinas 1 L/cil   criollo      3     3     3
##  4 Algas Marinas 1 L/cil   Hibrido      3     3     3
##  5 Algas Marinas 1,5 L/cil criollo      3     3     3
##  6 Algas Marinas 1,5 L/cil Hibrido      3     3     3
##  7 Azufre 100 gr.200 L-1   criollo      3     3     3
##  8 Azufre 100 gr.200 L-1   Hibrido      3     3     3
##  9 Azufre 150 gr.200 L-1   criollo      3     3     3
## 10 Azufre 150 gr.200 L-1   Hibrido      3     3     3
## 11 Suero de leche 10%      criollo      3     3     3
## 12 Suero de leche 10%      Hibrido      3     3     3
## 13 Suero de leche 30%      criollo      3     3     3
## 14 Suero de leche 30%      Hibrido      3     3     3

sm <- plantula %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(where(is.numeric), ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 15
## # Groups:   tratamiento [7]
##    tratamiento             variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
##    <fct>                   <fct>        <int>    <int>    <int>          <int>
##  1 Agua Destilada          criollo         15       15       15             15
##  2 Agua Destilada          hibrido         15       15       15             15
##  3 Algas Marinas 1 L/cil   criollo         15       15       15             15
##  4 Algas Marinas 1 L/cil   hibrido         15       15       15             15
##  5 Algas Marinas 1,5 L/cil criollo         15       15       15             15
##  6 Algas Marinas 1,5 L/cil hibrido         15       15       15             15
##  7 Azufre 100 gr.200 L-1   criollo         15       15       15             15
##  8 Azufre 100 gr.200 L-1   hibrido         15       15       15             15
##  9 Azufre 150 gr.200 L-1   criollo         15       15       15             15
## 10 Azufre 150 gr.200 L-1   hibrido         15       15       15             15
## 11 Suero de leche 10%      criollo         15       15       15             15
## 12 Suero de leche 10%      hibrido         15       15       15             15
## 13 Suero de leche 30%      criollo         15       15       15             15
## 14 Suero de leche 30%      hibrido         15       15       15             15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## #   nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## #   peso_fres_brote <int>, peso_seco_brote <int>

5 Objetivos

  1. Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

  2. Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

5.1 Objetivo Específico 1

Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

  • Imbibiciación, % germinación, velocidad e IG

5.1.1 Imbibición

trait <- "peso"
fb <- imbibition

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad +  (1 + tiempo|trat)") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tiempo +  tratamiento*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       bloque      tratamiento variedad    tiempo      trat       
##  [7] peso        resi        res_MAD     rawp.BHStud adjp        bholm      
## [13] out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: peso
##                        Df  Sum Sq Mean Sq  F value              Pr(>F)    
## bloque                  2  0.0021 0.00105   0.1222               0.885    
## tiempo                  4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## tratamiento             6  3.2174 0.53624  62.1186 <0.0000000000000002 ***
## variedad                1  0.6165 0.61649  71.4150 <0.0000000000000002 ***
## tratamiento:variedad    6  2.6467 0.44111  51.0987 <0.0000000000000002 ***
## Residuals            2080 17.9556 0.00863                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ tiempo|variedad|tratamiento) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
tiempo variedad tratamiento emmean SE df lower.CL upper.CL group
1 12 criollo Agua Destilada 0.8296986 0.0086019 2080 0.8128293 0.8465678 a
3 9 criollo Agua Destilada 0.8279129 0.0086019 2080 0.8110436 0.8447821 a
2 3 criollo Agua Destilada 0.7645081 0.0086019 2080 0.7476388 0.7813774 b
4 6 criollo Agua Destilada 0.7620295 0.0086019 2080 0.7451603 0.7788988 b
5 0 criollo Agua Destilada 0.6398176 0.0086019 2080 0.6229483 0.6566869 c
11 12 criollo Algas Marinas 1 L/cil 0.7157719 0.0086019 2080 0.6989026 0.7326412 a
13 9 criollo Algas Marinas 1 L/cil 0.7139862 0.0086019 2080 0.6971169 0.7308555 a
12 3 criollo Algas Marinas 1 L/cil 0.6505814 0.0086019 2080 0.6337122 0.6674507 b
14 6 criollo Algas Marinas 1 L/cil 0.6481029 0.0086019 2080 0.6312336 0.6649721 b
15 0 criollo Algas Marinas 1 L/cil 0.5258910 0.0086019 2080 0.5090217 0.5427602 c
21 12 criollo Algas Marinas 1,5 L/cil 0.6749719 0.0086019 2080 0.6581026 0.6918412 a
23 9 criollo Algas Marinas 1,5 L/cil 0.6731862 0.0086019 2080 0.6563169 0.6900555 a
22 3 criollo Algas Marinas 1,5 L/cil 0.6097814 0.0086019 2080 0.5929122 0.6266507 b
24 6 criollo Algas Marinas 1,5 L/cil 0.6073029 0.0086019 2080 0.5904336 0.6241721 b
25 0 criollo Algas Marinas 1,5 L/cil 0.4850910 0.0086019 2080 0.4682217 0.5019602 c
31 12 criollo Azufre 100 gr.200 L-1 0.6591052 0.0086019 2080 0.6422360 0.6759745 a
33 9 criollo Azufre 100 gr.200 L-1 0.6573195 0.0086019 2080 0.6404503 0.6741888 a
32 3 criollo Azufre 100 gr.200 L-1 0.5939148 0.0086019 2080 0.5770455 0.6107840 b
34 6 criollo Azufre 100 gr.200 L-1 0.5914362 0.0086019 2080 0.5745669 0.6083055 b
35 0 criollo Azufre 100 gr.200 L-1 0.4692243 0.0086019 2080 0.4523550 0.4860936 c
41 12 criollo Azufre 150 gr.200 L-1 0.6322386 0.0086019 2080 0.6153693 0.6491078 a
43 9 criollo Azufre 150 gr.200 L-1 0.6304529 0.0086019 2080 0.6135836 0.6473221 a
42 3 criollo Azufre 150 gr.200 L-1 0.5670481 0.0086019 2080 0.5501788 0.5839174 b
44 6 criollo Azufre 150 gr.200 L-1 0.5645695 0.0086019 2080 0.5477003 0.5814388 b
45 0 criollo Azufre 150 gr.200 L-1 0.4423576 0.0086019 2080 0.4254883 0.4592269 c
51 12 criollo Suero de leche 10% 0.8092386 0.0086019 2080 0.7923693 0.8261078 a
53 9 criollo Suero de leche 10% 0.8074529 0.0086019 2080 0.7905836 0.8243221 a
52 3 criollo Suero de leche 10% 0.7440481 0.0086019 2080 0.7271788 0.7609174 b
54 6 criollo Suero de leche 10% 0.7415695 0.0086019 2080 0.7247003 0.7584388 b
55 0 criollo Suero de leche 10% 0.6193576 0.0086019 2080 0.6024883 0.6362269 c
61 12 criollo Suero de leche 30% 0.7740386 0.0086019 2080 0.7571693 0.7909078 a
63 9 criollo Suero de leche 30% 0.7722529 0.0086019 2080 0.7553836 0.7891221 a
62 3 criollo Suero de leche 30% 0.7088481 0.0086019 2080 0.6919788 0.7257174 b
64 6 criollo Suero de leche 30% 0.7063695 0.0086019 2080 0.6895003 0.7232388 b
65 0 criollo Suero de leche 30% 0.5841576 0.0086019 2080 0.5672883 0.6010269 c
6 12 Hibrido Agua Destilada 0.7764386 0.0086019 2080 0.7595693 0.7933078 a
8 9 Hibrido Agua Destilada 0.7746529 0.0086019 2080 0.7577836 0.7915221 a
7 3 Hibrido Agua Destilada 0.7112481 0.0086019 2080 0.6943788 0.7281174 b
9 6 Hibrido Agua Destilada 0.7087695 0.0086019 2080 0.6919003 0.7256388 b
10 0 Hibrido Agua Destilada 0.5865576 0.0086019 2080 0.5696883 0.6034269 c
16 12 Hibrido Algas Marinas 1 L/cil 0.7279719 0.0086019 2080 0.7111026 0.7448412 a
18 9 Hibrido Algas Marinas 1 L/cil 0.7261862 0.0086019 2080 0.7093169 0.7430555 a
17 3 Hibrido Algas Marinas 1 L/cil 0.6627814 0.0086019 2080 0.6459122 0.6796507 b
19 6 Hibrido Algas Marinas 1 L/cil 0.6603029 0.0086019 2080 0.6434336 0.6771721 b
20 0 Hibrido Algas Marinas 1 L/cil 0.5380910 0.0086019 2080 0.5212217 0.5549602 c
26 12 Hibrido Algas Marinas 1,5 L/cil 0.7881052 0.0086019 2080 0.7712360 0.8049745 a
28 9 Hibrido Algas Marinas 1,5 L/cil 0.7863195 0.0086019 2080 0.7694503 0.8031888 a
27 3 Hibrido Algas Marinas 1,5 L/cil 0.7229148 0.0086019 2080 0.7060455 0.7397840 b
29 6 Hibrido Algas Marinas 1,5 L/cil 0.7204362 0.0086019 2080 0.7035669 0.7373055 b
30 0 Hibrido Algas Marinas 1,5 L/cil 0.5982243 0.0086019 2080 0.5813550 0.6150936 c
36 12 Hibrido Azufre 100 gr.200 L-1 0.7332386 0.0086019 2080 0.7163693 0.7501078 a
38 9 Hibrido Azufre 100 gr.200 L-1 0.7314529 0.0086019 2080 0.7145836 0.7483221 a
37 3 Hibrido Azufre 100 gr.200 L-1 0.6680481 0.0086019 2080 0.6511788 0.6849174 b
39 6 Hibrido Azufre 100 gr.200 L-1 0.6655695 0.0086019 2080 0.6487003 0.6824388 b
40 0 Hibrido Azufre 100 gr.200 L-1 0.5433576 0.0086019 2080 0.5264883 0.5602269 c
46 12 Hibrido Azufre 150 gr.200 L-1 0.7735052 0.0086019 2080 0.7566360 0.7903745 a
48 9 Hibrido Azufre 150 gr.200 L-1 0.7717195 0.0086019 2080 0.7548503 0.7885888 a
47 3 Hibrido Azufre 150 gr.200 L-1 0.7083148 0.0086019 2080 0.6914455 0.7251840 b
49 6 Hibrido Azufre 150 gr.200 L-1 0.7058362 0.0086019 2080 0.6889669 0.7227055 b
50 0 Hibrido Azufre 150 gr.200 L-1 0.5836243 0.0086019 2080 0.5667550 0.6004936 c
56 12 Hibrido Suero de leche 10% 0.7615719 0.0086019 2080 0.7447026 0.7784412 a
58 9 Hibrido Suero de leche 10% 0.7597862 0.0086019 2080 0.7429169 0.7766555 a
57 3 Hibrido Suero de leche 10% 0.6963814 0.0086019 2080 0.6795122 0.7132507 b
59 6 Hibrido Suero de leche 10% 0.6939029 0.0086019 2080 0.6770336 0.7107721 b
60 0 Hibrido Suero de leche 10% 0.5716910 0.0086019 2080 0.5548217 0.5885602 c
66 12 Hibrido Suero de leche 30% 0.7741052 0.0086019 2080 0.7572360 0.7909745 a
68 9 Hibrido Suero de leche 30% 0.7723195 0.0086019 2080 0.7554503 0.7891888 a
67 3 Hibrido Suero de leche 30% 0.7089148 0.0086019 2080 0.6920455 0.7257840 b
69 6 Hibrido Suero de leche 30% 0.7064362 0.0086019 2080 0.6895669 0.7233055 b
70 0 Hibrido Suero de leche 30% 0.5842243 0.0086019 2080 0.5673550 0.6010936 c

p1a <- mc %>% 
  plot_smr(type = "line"
           , x = "tiempo"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Peso (g)"
           , xlab = "Tiempo (h)"
           , ylimits = c(0, 1, 0.2)
           ) + 
  facet_wrap(. ~ tratamiento, ncol = 2)

p1a

5.1.2 Porcentaje de Germination

trait <- "pg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tratamiento*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       bloque      tratamiento variedad    pg          resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: pg
##                      Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque                2   633.3   316.7  0.6222 0.544582   
## tratamiento           6  7000.0  1166.7  2.2922 0.065673 . 
## variedad              1  4609.5  4609.5  9.0565 0.005753 **
## tratamiento:variedad  6  6857.1  1142.9  2.2454 0.070466 . 
## Residuals            26 13233.3   509.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|tratamiento) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad tratamiento emmean SE df lower.CL upper.CL group
2 criollo Agua Destilada 86.66667 13.02529 26 59.8928044 113.44053 a
1 Hibrido Agua Destilada 63.33333 13.02529 26 36.5594710 90.10720 a
3 Hibrido Algas Marinas 1 L/cil 70.00000 13.02529 26 43.2261377 96.77386 a
4 criollo Algas Marinas 1 L/cil 30.00000 13.02529 26 3.2261377 56.77386 b
5 Hibrido Algas Marinas 1,5 L/cil 56.66667 13.02529 26 29.8928044 83.44053 a
6 criollo Algas Marinas 1,5 L/cil 36.66667 13.02529 26 9.8928044 63.44053 a
7 Hibrido Azufre 100 gr.200 L-1 66.66667 13.02529 26 39.8928044 93.44053 a
8 criollo Azufre 100 gr.200 L-1 16.66667 13.02529 26 -10.1071956 43.44053 b
9 Hibrido Azufre 150 gr.200 L-1 76.66667 13.02529 26 49.8928044 103.44053 a
10 criollo Azufre 150 gr.200 L-1 26.66667 13.02529 26 -0.1071956 53.44053 b
11 Hibrido Suero de leche 10% 70.00000 13.02529 26 43.2261377 96.77386 a
12 criollo Suero de leche 10% 70.00000 13.02529 26 43.2261377 96.77386 a
13 Hibrido Suero de leche 30% 43.33333 13.02529 26 16.5594710 70.10720 a
14 criollo Suero de leche 30% 33.33333 13.02529 26 6.5594710 60.10720 a

p1b <- mc %>% 
  plot_smr(type = "bar"
           , x = "tratamiento"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germinación ('%')"
           , xlab = "Tratamientos"
           , ylimits = c(0, 120, 20)
           ) 

p1b

5.1.3 Velocidad de germinación

trait <- "vg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tratamiento*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##    index bloque             tratamiento variedad vg      resi   res_MAD
## 7      7      1 Algas Marinas 1,5 L/cil  criollo  1 -1.666667 -3.372454
## 34    34      1   Azufre 150 gr.200 L-1  Hibrido  5  1.888889  3.822114
##     rawp.BHStud         adjp       bholm out_flag
## 7  0.0007450159 0.0007450159 0.030545650  OUTLIER
## 34 0.0001323123 0.0001323123 0.005557118  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: vg
##                      Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque                2  1.3321 0.66607  1.3664 0.274150   
## tratamiento           6  8.6594 1.44323  2.9608 0.026214 * 
## variedad              1  2.0003 2.00025  4.1035 0.054051 . 
## tratamiento:variedad  6 11.5622 1.92703  3.9533 0.006872 **
## Residuals            24 11.6989 0.48745                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|tratamiento) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad tratamiento emmean SE df lower.CL upper.CL group
2 criollo Agua Destilada 2.888889 0.4030931 24 2.0569455 3.720832 a
1 Hibrido Agua Destilada 2.333333 0.4030931 24 1.5013900 3.165277 a
3 Hibrido Algas Marinas 1 L/cil 3.277778 0.4030931 24 2.4458344 4.109721 a
4 criollo Algas Marinas 1 L/cil 1.500000 0.4030931 24 0.6680567 2.331943 b
6 criollo Algas Marinas 1,5 L/cil 3.379630 0.5004960 24 2.3466566 4.412603 a
5 Hibrido Algas Marinas 1,5 L/cil 2.833333 0.4030931 24 2.0013900 3.665277 a
7 Hibrido Azufre 100 gr.200 L-1 3.833333 0.4030931 24 3.0013900 4.665277 a
8 criollo Azufre 100 gr.200 L-1 1.666667 0.4030931 24 0.8347233 2.498610 b
9 Hibrido Azufre 150 gr.200 L-1 2.046296 0.5004960 24 1.0133232 3.079269 a
10 criollo Azufre 150 gr.200 L-1 1.333333 0.4030931 24 0.5013900 2.165277 a
12 criollo Suero de leche 10% 3.055556 0.4030931 24 2.2236122 3.887499 a
11 Hibrido Suero de leche 10% 3.000000 0.4030931 24 2.1680567 3.831943 a
14 criollo Suero de leche 30% 2.333333 0.4030931 24 1.5013900 3.165277 a
13 Hibrido Suero de leche 30% 1.833333 0.4030931 24 1.0013900 2.665277 a

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "tratamiento"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Velocidad de germinación (días)"
           , xlab = "Tratamientos"
           , ylimits = c(0, 6, 1)
           ) 

p1c

5.1.4 Indice de germinación

trait <- "ig"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + tratamiento*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tratamiento*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##    index bloque           tratamiento variedad  ig      resi  res_MAD
## 25    25      1 Algas Marinas 1 L/cil  Hibrido 0.2 -1.466667 -3.29751
##     rawp.BHStud         adjp      bholm out_flag
## 25 0.0009754607 0.0009754607 0.04096935  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ig
##                      Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque                2  0.3050  0.1525  0.4149 0.664896   
## tratamiento           6 10.3540  1.7257  4.6949 0.002507 **
## variedad              1  3.8850  3.8850 10.5697 0.003278 **
## tratamiento:variedad  6  6.5489  1.0915  2.9695 0.024965 * 
## Residuals            25  9.1890  0.3676                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|tratamiento) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad tratamiento emmean SE df lower.CL upper.CL group
2 criollo Agua Destilada 2.6000000 0.3500293 25 1.8791012 3.3208988 a
1 Hibrido Agua Destilada 1.7666667 0.3500293 25 1.0457678 2.4875655 a
3 Hibrido Algas Marinas 1 L/cil 2.3679487 0.4341579 25 1.4737838 3.2621137 a
4 criollo Algas Marinas 1 L/cil 0.6000000 0.3500293 25 -0.1208988 1.3208988 b
5 Hibrido Algas Marinas 1,5 L/cil 1.1333333 0.3500293 25 0.4124345 1.8542322 a
6 criollo Algas Marinas 1,5 L/cil 0.5666667 0.3500293 25 -0.1542322 1.2875655 a
7 Hibrido Azufre 100 gr.200 L-1 1.2333333 0.3500293 25 0.5124345 1.9542322 a
8 criollo Azufre 100 gr.200 L-1 0.1666667 0.3500293 25 -0.5542322 0.8875655 b
9 Hibrido Azufre 150 gr.200 L-1 1.9666667 0.3500293 25 1.2457678 2.6875655 a
10 criollo Azufre 150 gr.200 L-1 0.5333333 0.3500293 25 -0.1875655 1.2542322 b
11 Hibrido Suero de leche 10% 1.7000000 0.3500293 25 0.9791012 2.4208988 a
12 criollo Suero de leche 10% 1.6666667 0.3500293 25 0.9457678 2.3875655 a
13 Hibrido Suero de leche 30% 1.0666667 0.3500293 25 0.3457678 1.7875655 a
14 criollo Suero de leche 30% 0.5333333 0.3500293 25 -0.1875655 1.2542322 a

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "tratamiento"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Indice de germinación"
           , xlab = "Tratamientos"
           , ylimits = c(0, 5, 1)
           ) 

p1d

5.2 Figura 1

legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)

p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 1
            , labels = c("b", "c", "d")
            ) 

p1il <- list(legend, p1i) %>% 
  plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))


list(p1a, p1il) %>% 
  plot_grid(plotlist = ., ncol = 2, rel_widths = c(1.2, 1.8), labels = c("a")) %>% 
  ggsave2(plot = ., "files/Fig-1.jpg"
         , units = "cm"
         , width = 40
         , height = 25
         )

knitr::include_graphics("files/Fig-1.jpg")